RIKEN Center for Advanced Intelligence Project Robotics for Infrastructure Management Team

Team Leader: Takayuki Okatani (D.Eng.)

Japanese Page

Research Summary

Improving the efficiency of infrastructure inspection and maintenance, such as for bridges, is an urgent issue. Our team focuses on developing AI and robotic technologies, including deep learning-based image recognition, spatial navigation, and multimodal AI. This AI evaluates structural health and detects abnormalities from drone-captured images, leveraging the expertise of skilled inspectors. It also enables natural language interaction with users and other AI agents. Beyond infrastructure maintenance, its applications extend to various challenges, such as disaster risk assessment and enhancing mobility safety, aiming to address diverse issues through advanced AI solutions.

Research Subjects:

Robotics for Infrastructure Inspection and Management
Understanding and Improvement of Deep Learning

Main Research Fields

Computer Science

Related Research Fields

Engineering
Neuroscience & Behavior
Mathematics

Keywords

AI and Robotic Technologies for Infrastructure Inspection and Management
Real-World Applications of Multimodal AI

Selected Publications

1. Charoenpitaks, Korawat, Van-Quang Nguyen, Masanori Suganuma, Masahiro Takahashi, Ryoma Niihara, and Takayuki Okatani.:
"Exploring the Potential of Multi-Modal AI for Driving Hazard Prediction"
IEEE Transactions on Intelligent Vehicles, Early Access, 1-11 (2024).
2. Yamane, Tatsuro, Pang-jo Chun, Ji Dang, and Takayuki Okatani.:
"Deep learning-based bridge damage cause estimation from multiple images using visual question answering"
Structure and Infrastructure Engineering 1-14 (2024)
3. Kunlamai, Thannarot, Tatsuro Yamane, Masanori Suganuma, Pang‐Jo Chun, and Takayaki Okatani.:
"Improving visual question answering for bridge inspection by pre‐training with external data of image–text pairs"
Computer‐Aided Civil and Infrastructure Engineering 39, no. 3, 345-361 (2024)
4. Zhang, Jie, Masanori Suganuma, and Takayuki Okatani.:
"Contextual affinity distillation for image anomaly detection"
In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 149-158 (2024)
5. Lu, Xiangyong, Masanori Suganuma, and Takayuki Okatani.:
"SBCFormer: Lightweight Network Capable of Full-size ImageNet Classification at 1 FPS on Single Board Computers"
In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 1123-1133 (2024)
6. Wang, Zhijie, Masanori Suganuma, and Takayuki Okatani.:
"Rethinking unsupervised domain adaptation for semantic segmentation"
Pattern Recognition Letters 186, 119-125 (2024)
7. Aota, Toshimichi, Lloyd Teh Tzer Tong, and Takayuki Okatani.:
"Zero-shot versus many-shot: Unsupervised texture anomaly detection"
In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 5564-5572 (2023)
8. Zhu, Yanjie, Hidehiko Sekiya, Takayuki Okatani, Masayuki Tai, and Shogo Morichika.:
"B-CNN: a deep learning method for accelerometer-based fatigue cracks monitoring system"
Journal of Civil Structural Health Monitoring 13, no. 4, 947-959 (2023)
9. Wang, Zhijie, Xing Liu, Masanori Suganuma, and Takayuki Okatani.:
"Unsupervised domain adaptation for semantic segmentation via cross-region alignment"
Computer Vision and Image Understanding 234 (2023)
10. Nguyen, Van-Quang, Masanori Suganuma, and Takayuki Okatani.:
"Grit: Faster and better image captioning transformer using dual visual features"
In European Conference on Computer Vision, 167-184 (2022)

Lab Members

Principal investigator

Takayuki Okatani: Team Leader

Core members

Quang Van Nguyen: Postdoctoral Researcher
Zhijie Wang: Postdoctoral Researcher
Hidehiko Sekiya: Visiting Scientist
Pang-jo Chun: Visiting Scientist
Xiangyong Lu: Research Part-time Worker I

Careers

Position	Deadline
Seeking a Research Scientist or Postdoctoral Researcher (W24144)	Open until filled

Contact Information

Aramaki Aza Aoba, Aoba-ku,
980-8579 Sendai, Miyagi,
Japan
Email: takayuki.okatani@riken.jp